What Makes a Good Dataset for Symbol Description Reading?
Not on the map. Why?
Abstract (arXiv)
The usage of mathematical formulas as concise representations of a document's key ideas is common practice. Correctly interpreting these formulas, by identifying mathematical symbols and extracting their descriptions, is an important task in document understanding. This paper makes the following contributions to the mathematical identifier description reading (MIDR) task: (i) introduces the Math Formula Question Answering Dataset (MFQuAD) with 7508 annotated identifier occurrences; (ii) describes novel variations of the noun phrase ranking approach for the MIDR task; (iii) reports experimental results for the SOTA noun phrase ranking approach and our novel variations of the approach, providing problem insights and a performance baseline; (iv) provides a position on the features that make an effective dataset for the MIDR task.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 17 Apr 2023.
Builds on
8 of 28 references.
References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
- 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
- 2018Know What You Don't Know: Unanswerable Questions for SQuADcited by 1,538
- 2014Mathematical Language Processing Projectcited by 6
- 2022Symlink: A New Dataset for Scientific Symbol-Description Linkingcited by 2
Built on by
Not cited yet.